References
- Liaqat S., Dashtipour K., Arshad K., Assaleh K. & Ramzan N. (2021). A hybrid posture detection framework: Integrating machine learning and deep neural networks, IEEE Sensors Journal, 9515-9522.
- Kendall F., McCreary E. & Provance, P. (2005). Muscles testing and function with posture and pain.,USA: Lippincott Williams and Wilkins, 560.
- Gilman S. (2014). “Stand Up Straight”: Notes Toward a History of Posture, J. Med. Humanit., 57–83.
- Maciałczyk-Paprocka K., Stawińska-Witoszyńska B., Kotwicki T., Sowińska A., Krzyżaniak A., Walkowiak J. & Krzywińska-Wiewiorowska M. (2017). Prevalence of incorrect body posture in children and adolescents with overweight and obesity, European journal of pediatrics, 563–572.
- Mahesh B. (2020). Machine Learning Algorithms-A Review, International Journal of Science and Research (IJSR), 381-386.
- Jordan M. I., & Mitchell T. M. (2015). Machine learning: Trends, perspectives, and prospects., Science, 255-260.
- Oppermann A. (2019). What is Deep Learning and How does it work? Retrieved from Towards Data Science, https://towardsdatascience.com/what-is-deep-learning-and-how-does-it-work-2ce44bb692ac, accessed on October 7, 2021.
- Goodfellow I., Bengio Y. & Courville A. (2016). Machine learning basics, Deep learning, 98-164.
- Saha S., Pal M., Konar A., & Janarthanan R. (2013). Neural network based gesture recognition for elderly health care using kinect sensor. International Conference on Swarm, Evolutionary, and Memetic Computing (pg. 376-386). Cham: Springer.
- Song Y., Gu Y., Wang P., Liu Y., & Li A. (2013). A Kinect based gesture recognition algorithm using GMM and HMM, 6th International Conference on Biomedical Engineering and Informatics, Institute of Electrical and Electronics Engineers (IEEE), 750-754.
- Zhang Z., Liu Y., Li A. & Wang M. (2014). A novel method for user-defined human posture recognition using Kinect, 7th International Congress on image and signal processing, Institute of Electrical and Electronics Engineers (IEEE), 736-740.
- Zemp R., Tanadini M., Plüss S., Schnüriger K., Singh N. B., Taylor W. R. & Lorenzetti S. (2016). Application of machine learning approaches for classifying sitting posture based on force and acceleration sensors, BioMed Research international, 1-9.
- Zerrouki N., Harrou F., Houacine A. & Sun Y. (2016). Fall detection using supervised machine learning algorithms: A comparative study, 8th International Conference on Modelling, Identification and Control (ICMIC), Institute of Electrical and Electronics Engineers (IEEE), 665-670.
- Ma C., Li W., Gravina R. & Fortino G. (2017). Posture detection based on smart cushion for wheelchair users, Sensors, 719.
- Kim Y. M., Son Y., Kim W., Jin B. & Yun M. H. (2018). Classification of children’s sitting postures using machine learning algorithms, Applied Sciences, 1280.
- Roh J., Park H. J., Lee K. J., Hyeong J., Kim S. & Lee B. (2018). Sitting posture monitoring system based on a low-cost load cell using machine learning. Sensors, 208.
- Rosero-Montalvo P. D., Peluffo-Ordóñez D. H., Batista V. F., Serrano J. & Rosero E. A. (2018). Intelligent system for identification of wheelchair user’s posture using machine learning techniques, IEEE Sensors Journal, 1936-1942.
- Mallick T., Das P. P. & Majumdar A. K. (2019). Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach, ArXiv, abs/1909.11023.
- Koubâa A., Ammar A., Benjdira B., Al-Hadid A., Kawaf B., Al-Yahri S. A. & ... & Ras M. B. (2020). Activity monitoring of islamic prayer (salat) postures using deep learning, 6th Conference on Data Science and Machine Learning Applications (CDMA), Institute of Electrical and Electronics Engineers (IEEE), 106-111.
- Agrawal Y., Shah Y. & Sharma A. (2020). Implementation of machine learning technique for identification of yoga poses, IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), Institute of Electrical and Electronics Engineers (IEEE), 40-43.
- Han J., Song W., Gozho A., Sung Y., Ji S., Song L. & Zhang Q. (2020). Lora-based smart IoT application for smart city: an example of human posture detection. Wireless Communications and Mobile Computing., Art. no. 8822555.
- Lee J., Joo H., Lee J. & Chee Y. (2020). Automatic classification of squat posture using inertial sensors: Deep learning approach. Sensors, 361.
- Ahmad J., Sidén J. & Andersson H. (2021). A Proposal of Implementation of Sitting Posture Monitoring System for Wheelchair Utilizing Machine Learning Methods. Sensors, 6349.
- Kumar S. S., Dashtipour K., Gogate M., Ahmad J., Assaleh K., Arshad K. & Ahmad W. (2021). Comparing the Performance of Different Classifiers for Posture Detection. EAI.